Time-frequency analysis of click-evoked otoacoustic emissions by means of a minimum variance spectral estimation-based method.

This paper proposes a new minimum variance spectral estimation (MVSE)-based time-frequency analysis (TFA) technique for click-evoked otoacoustic emissions (CEOAEs). The MVSE is a popular spectrum analysis method which can yield a high frequency resolution compared to other nonparametric spectral analysis procedures. The conventional MVSE is extended to a TFA method by windowing the observation data to obtain a time-frequency representation for the signal under study. Inspired by the adaptive window selection process in wavelet transform and based on the time-frequency characteristics of CEOAEs, the window size of the windowed MVSE (WMVSE) is given a small value at high frequencies and a large value at low frequencies. The adaptive window size selection yields the proposed frequency-dependent WMVSE (FDWMVSE). The FDWMVSE method integrates the advantages of the adaptive window selection in wavelet transform with the fine frequency resolution of MVSE. Experimental results show that the FDWMVSE can achieve satisfactory time-frequency resolution and reveal meaningful time-frequency features when applied to synthesized and real CEOAEs.